scholarly journals Sparse generalized eigenvalue problem with application to canonical correlation analysis for integrative analysis of methylation and gene expression data

Biometrics ◽  
2018 ◽  
Vol 74 (4) ◽  
pp. 1362-1371 ◽  
Author(s):  
Sandra E. Safo ◽  
Jeongyoun Ahn ◽  
Yongho Jeon ◽  
Sungkyu Jung
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Eigenvalue Problem ◽  
Canonical Correlation Analysis ◽  
Gene Expression Data ◽  
Canonical Correlation ◽  
Integrative Analysis ◽  
Generalized Eigenvalue Problem ◽  
Expression Data ◽  
Generalized Eigenvalue

scholarly journals Expression reflects population structure

2018 ◽  
Author(s):  
Brielin C Brown ◽  
Nicolas L. Bray ◽  
Lior Pachter
Keyword(s):  
Gene Expression ◽  
Population Structure ◽  
African Americans ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Gene Expression Data ◽  
Principal Components ◽  
Canonical Correlation ◽  
Principal Component ◽  
Expression Data

AbstractPopulation structure in genotype data has been extensively studied, and is revealed by looking at the principal components of the genotype matrix. However, no similar analysis of population structure in gene expression data has been conducted, in part because a naïve principal components analysis of the gene expression matrix does not cluster by population. We identify a linear projection that reveals population structure in gene expression data. Our approach relies on the coupling of the principal components of genotype to the principal components of gene expression via canonical correlation analysis. Futhermore, we analyze the variance of each gene within the projection matrix to determine which genes significantly influence the projection. We identify thousands of significant genes, and show that a number of the top genes have been implicated in diseases that disproportionately impact African Americans.Author SummaryHigh dimensional, multi-modal genomics datasets are becoming increasingly common, which warrants investigation into analysis techniques that can reveal structure in the data without over-fitting. Here, we show that the coupling of principal component analysis to canonical correlation analysis offers an efficient approach to exploratory analysis of this kind of data. We apply this method to the GEUVADIS dataset of genotype and gene expression values of European and Yoruban individuals, finding as-of-yet unstudied population structure in the gene expression values. Moreover, many of the top genes identified by our method have been previously implicated in diseases that disproportionately impact African Americans.

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Chen-An Tsai ◽  
James J. Chen
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Gene Expression Data ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Superior Performance ◽  
Expression Data ◽  
Gene Set ◽  
Gene Sets ◽  
Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

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